| 1. |
- Biermann, Jana, et al.
(författare)
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A 17-marker panel for global genomic instability in breast cancer.
- 2020
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Ingår i: Genomics. - : Elsevier BV. - 0888-7543 .- 1089-8646. ; 112:2, s. 1151-1161
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Tidskriftsartikel (refereegranskat)abstract
- Genomic instability is a hallmark of cancer that plays a pivotal role in breast cancer development and evolution. A number of existing prognostic gene expression signatures for breast cancer are based on proliferation-related genes. Here, we identified a 17-marker panel associated with genome stability. A total of 136 primary breast carcinomas were stratified by genome stability. Matched gene expression profiles showed an innate segregation based on genome stability. We identified a 17-marker panel stratifying the training and validation cohorts into high- and low-risk patients. The 17 genes associated with genomic instability strongly impacted clinical outcome in breast cancer. Pathway analyses determined chromosome organisation, cell cycle regulation, and RNA processing as the underlying biological processes, thereby offering options for drug development and treatment tailoring. Our work supports the applicability of the 17-marker panel to improve clinical outcome prediction for breast cancer patients based on a signature accounting for genomic instability.
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| 2. |
- Biermann, Jana, et al.
(författare)
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A novel 18-marker panel predicting clinical outcome in breast cancer
- 2017
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Ingår i: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. - 1538-7755. ; 26:11, s. 1619-28
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiling has made considerable contributions to our understanding of cancer biology and clinical care. This study describes a novel gene expression signature for breast cancer-specific survival that was validated using external datasets. Gene expression signatures for invasive breast carcinomas (mainly Luminal B subtype) corresponding to 136 patients were analysed using Cox regression and the effect of each gene on disease-specific survival (DSS) was estimated. Iterative Bayesian Model Averaging was applied on multivariable Cox regression models resulting in an 18-marker panel, which was validated using three external validation datasets. The 18 genes were analysed for common pathways and functions using the Ingenuity Pathway Analysis software. This study complied with the REMARK criteria. The 18-gene multivariable model showed a high predictive power for DSS in the training and validation cohort and a clear stratification between high- and low-risk patients. The differentially expressed genes were predominantly involved in biological processes such as cell cycle, DNA replication, recombination, and repair. Furthermore, the majority of the 18 genes were found to play a pivotal role in cancer. Our findings demonstrated that the 18 molecular markers were strong predictors of breast cancer-specific mortality. The stable time-dependent area under the ROC curve function (AUC(t)) and high C-indices in the training and validation cohorts were further improved by fitting a combined model consisting of the 18-marker panel and established clinical markers. Our work supports the applicability of this 18-marker panel to improve clinical outcome prediction for breast cancer patients.
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| 3. |
- Biermann, Jana, et al.
(författare)
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Clonal relatedness in tumour pairs of breast cancer patients.
- 2018
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Ingår i: Breast cancer research : BCR. - : Springer Science and Business Media LLC. - 1465-542X. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Molecular classification of tumour clonality is currently not evaluated in multiple invasive breast carcinomas, despite evidence suggesting common clonal origins. There is no consensus about which type of data (e.g. copy number, mutation, histology) and especially which statistical method is most suitable to distinguish clonal recurrences from independent primary tumours.Thirty-seven invasive breast tumour pairs were stratified according to laterality and time interval between the diagnoses of the two tumours. In a multi-omics approach, tumour clonality was analysed by integrating clinical characteristics (n = 37), DNA copy number (n = 37), DNA methylation (n = 8), gene expression microarray (n = 7), RNA sequencing (n = 3), and SNP genotyping data (n = 3). Different statistical methods, e.g. the diagnostic similarity index (SI), were used to classify the tumours as clonally related recurrences or independent primary tumours.The SI and hierarchical clustering showed similar tendencies and the highest concordance with the other methods. Concordant evidence for tumour clonality was found in 46% (17/37) of patients. Notably, no association was found between the current clinical guidelines and molecular tumour features.A more accurate classification of clonal relatedness between multiple breast tumours may help to mitigate treatment failure and relapse by integrating tumour-associated molecular features, clinical parameters, and statistical methods. Guidelines need to be defined with exact thresholds to standardise clonality testing in a routine diagnostic setting.
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| 4. |
- Biermann, Jana, et al.
(författare)
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Radiation-induced genomic instability in breast carcinomas of the Swedish haemangioma cohort.
- 2019
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Ingår i: Genes, chromosomes & cancer. - : Wiley. - 1098-2264 .- 1045-2257. ; 58:9, s. 627-35
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Tidskriftsartikel (refereegranskat)abstract
- Radiation-induced genomic instability (GI) is hypothesized to persist after exposure and ultimately promote carcinogenesis. Based on the absorbed dose to the breast, an increased risk of developing breast cancer was shown in the Swedish haemangioma cohort that was treated with radium-226 for skin haemangioma as infants. Here, we screened 31 primary breast carcinomas for genetic alterations using the OncoScan CNV Plus Assay to assess GI and chromothripsis-like patterns associated with the absorbed dose to the breast. Higher absorbed doses were associated with increased numbers of copy number alterations (CNAs) in the tumour genome and thus a more unstable genome. Hence, the observed dose-dependent GI in the tumour genome is a measurable manifestation of the long-term effects of irradiation. We developed a highly predictive Cox regression model for overall survival based on the interaction between absorbed dose and GI. The Swedish haemangioma cohort is a valuable cohort to investigate the biological relationship between absorbed dose and GI in irradiated humans. This work gives a biological basis for improved risk assessment to minimize carcinogenesis as a secondary disease after radiation therapy. This article is protected by copyright. All rights reserved.
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| 5. |
- Biermann, Jana, et al.
(författare)
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Tumour clonality in paired invasive breast carcinomas
- 2019
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Ingår i: Cancer Research. - 0008-5472.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Multiple invasive breast tumours may represent either independent primary tumours or clonal recurrences of the first tumour, where the same progenitor cell gives rise to all of the detected tumours. Consequently, the driver events for the progenitor cell need to have been identical in early tumour development. Molecular classification of tumour clonality is not currently evaluated in multiple invasive breast carcinomas, despite evidence suggesting common clonal origins. Furthermore, there is no consensus about which type of biological data (e.g. copy number, mutation, histology) and especially which statistical method is most suitable to distinguish clonal recurrences from independent primary tumours. Methods: Thirty-seven invasive breast tumour pairs were stratified by laterality (bilateral vs. ipsilateral) and the time interval between the diagnoses of the first and second tumours (synchronous vs. metachronous). Both tumours from the same patient were analysed by integrating clinical characteristics (n = 37), DNA copy number (n = 37), DNA methylation (n = 8), gene expression microarray (n = 7), RNA sequencing (n = 3), and SNP genotyping data (n = 3). Different statistical methods, e.g. the diagnostic similarity index (SI), distance measure, shared segment analysis etc., were used to classify the tumours from the same patient as clonally related recurrences or independent primary tumours. Results: The SI applied on DNA copy numbers derived from aCGH (array comparative genomic hybridization) data was determined as the strongest indicator of clonal relatedness as it showed the highest concordance with all other methods. The distance measure was the most conservative method and the shared segment analysis most liberal. Concordant evidence for tumour clonality was found in 46% (17/37) of the patients. Notably, no significant association was found between the clinical characteristics and molecular tumour features. Conclusions: A more accurate classification of clonal relatedness between multiple breast tumours may help to mitigate treatment failure and relapse by integrating tumour-associated molecular features, clinical parameters, and statistical methods. In cases of extremely similar or different tumour pairs, the results showed consistency regardless of the method used. The SI can be easily integrated into clinical routine using FFPE samples to obtain copy number data. However, clinical guidelines with exact thresholds need to be defined to standardize clonality testing in a routine diagnostic setting.
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| 6. |
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| 7. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Immunohistochemical validation of COL3A1, GPR158 and PITHD1 as prognostic biomarkers in early-stage ovarian carcinomasn
- 2019
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 19:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Ovarian cancer is the main cause of gynecological cancer-associated death. However, 5- Methods: Here, we evaluated the prognostic role of 29 genes for early-stage (I and II) ovarian Results: We provide evidence of aberrant protein expression patterns for Collagen type III alpha 1 Conclusions: The novel biomarkers identified here may improve prognostication at the time of
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| 8. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.
- 2020
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Ovarian cancer comprises multiple subtypes (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with differing molecular and clinical behavior. However, robust histotype-specific biomarkers for clinical use have yet to be identified. Here, we utilized a multi-omics approach to identify novel histotype-specific genetic markers associated with ovarian carcinoma histotypes (CCC, EC, HGSC, and MC) using DNA methylation, DNA copy number alteration and RNA sequencing data for 96 primary invasive early-stage (stage I and II) ovarian carcinomas. More specifically, the DNA methylation analysis revealed hypermethylation for CCC in comparison with the other histotypes. Moreover, copy number imbalances and novel chromothripsis-like rearrangements (n = 64) were identified in ovarian carcinoma, with the highest number of chromothripsis-like patterns in HGSC. For the 1000 most variable transcripts, underexpression was most prominent for all histotypes in comparison with normal ovarian samples. Overall, the integrative approach identified 46 putative oncogenes (overexpressed, hypomethylated and DNA gain) and three putative tumor suppressor genes (underexpressed, hypermethylated and DNA loss) when comparing the different histotypes. In conclusion, the current study provides novel insights into molecular features associated with early-stage ovarian carcinoma that may improve patient stratification and subclassification of the histotypes.
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| 9. |
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| 10. |
- Engqvist, Hanna, 1985, et al.
(författare)
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Validation of Novel Prognostic Biomarkers for Early-Stage Clear-Cell, Endometrioid and Mucinous Ovarian Carcinomas Using Immunohistochemistry
- 2020
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Early-stage (I and II) ovarian carcinoma patients generally have good prognosis. Yet, some patients die earlier than expected. Thus, it is important to stratify early-stage patients into risk groups to identify those in need of more aggressive treatment regimens. The prognostic value of 29 histotype-specific biomarkers identified using RNA sequencing was evaluated for early-stage clear-cell (CCC), endometrioid (EC) and mucinous (MC) ovarian carcinomas (n = 112) using immunohistochemistry on tissue microarrays. Biomarkers with prognostic significance were further evaluated in an external ovarian carcinoma data set using the web-based Kaplan-Meier plotter tool. Here, we provide evidence of aberrant protein expression patterns and prognostic significance of 17 novel histotype-specific prognostic biomarkers [10 for CCC (ARPC2, CCT5, GNB1, KCTD10, NUP155, RPL13A, RPL37, SETD3, SMYD2, TRIO), three for EC (CECR1, KIF26B, PIK3CA), and four for MC (CHEK1, FOXM1, KIF23, PARPBP)], suggesting biological heterogeneity within the histotypes. Combined predictive models comprising the protein expression status of the validated CCC, EC and MC biomarkers together with established clinical markers (age, stage, CA125, ploidy) improved the predictive power in comparison with models containing established clinical markers alone, further strengthening the importance of the biomarkers in ovarian carcinoma. Further, even improved predictive powers were demonstrated when combining these models with our previously identified prognostic biomarkers PITHD1 (CCC) and GPR158 (MC). Moreover, the proteins demonstrated improved risk prediction of CCC-, EC-, and MC-associated ovarian carcinoma survival. The novel histotype-specific prognostic biomarkers may not only improve prognostication and patient stratification of early-stage ovarian carcinomas, but may also guide future clinical therapy decisions.
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